Term Weighting Classification System Using the Chi-square Statistic for the Classification Subtask at NTCIR-6 Patent Retrieval Task

In the present paper, a term weighting classification method using the chi-square statistic is proposed and evaluated in the classification subtask at NTCIR-6 patent retrieval task. In this task, large numbers of patent applications are classified into Fterm categories. Therefore, a patent classification system requires high classification speed, as well as high classification accuracy. The chi-square statistic can calculate the frequency of word appearance in the F-term and the frequency of word non-appearance in the F-term. The proposed method treats words as a scalar value and a ranking algorithm simply adds the word values of each word included in the test patent document in each F-term. Therefore, the proposed method provides classification that is significantly faster than other methods. The proposed method is evaluated in A-precision, R-precision, and F-measure. Although the proposed method did not obtain the best score, this method achieves a classification accuracy that is as high as those of other methods using machine learning or the vector classification method. In this task, the processing speed is not evaluated. Therefore, processing speed is also evaluated. The evaluation results show that the proposed method is much faster than that using the vector classification method. Evaluation results of classification accuracy and processing speed show that the proposed method is confirmed to be effective and to be practical.